Deep Learning-Based Segmentation for Digital Epidermal Microscopic Images: A Comparative Study of Overall Performance

Digital epidermal microscopic (DEM) images offer the potential to quantitatively analyze skin aging at the microscopic level. However, stochastic complexity, local highlights, and low contrast in DEM images pose significant challenges to accurate segmentation. This study evaluated eight deep learning models to identify methods capable of accurately segmenting complex DEM images while meeting diverse performance requirements. To this end, this study first constructed a manually labeled DEM image dataset. Then, eight deep learning models (FCN-8s, SegNet, UNet, ResUNet, NestedUNet, DeepLabV3+, TransUNet, and AttentionUNet) were systematically evaluated for their performance in DEM image segmentation. Our experimental findings show that AttentionUNet achieves the highest segmentation accuracy, with a DSC of 0.8696 and an IoU of 0.7703. In contrast, FCN-8s is a better choice for efficient segmentation due to its lower parameter count (18.64 M) and efficient inference speed (GPU time 37.36 ms). FCN-8s and NestedUNet show a better balance between accuracy and efficiency when assessed across metrics like segmentation accuracy, model size, and inference time. Through a systematic comparison of eight deep learning models, this study identifies superior methods for segmenting skin furrows and ridges in DEM images. This work lays the foundation for subsequent applications, such as analyzing skin aging through furrow and ridge features.